Review: „Multiple Target Detection and Tracking with Guaranteed Framerates on Mobile Phones“

The described related work tracks motion using optical flow algorithms. It seems that those produce satisfying results but not yet cover the full potential of a AR tracking system. Others use interest-point based algorithms which are commonly known as very computational expensive. SIFT descriptors are probably the most used ones although they might belong the the most expensive ones. Nevertheless, some improvements have been achieved with SIFT and also SURF algorithms.

Similar to PTAM Wagner et. al also use a separated detection and tracking system. The detection system tries to find known targets in the currently available camera image using a modified SIFT algorithm. Instead of calculating the kind of expensive Differences of Gaussian (DoG) they use a FAST corner detection over multiple scales. Memory consumption is then reduced by using only 36-dimensional features instead of the original 128-dimensions of SIFT. Found descriptors are matched with entries from multiple spill trees, which is a similar data structure like the k-d-tree used in the original SIFT.

-- unfinished --


Experiment #1 - JavaSIFT

Just tried to do a first experiemnt using the ImageJ plugin JavaSIFT.

I took nine pictures* of my house with my smartphones camera. The goal was to let JavaSIFT reigster some interest points and then to see what I can do with that (JavaSIFT has some "align images" function). Turned out that the plugin is kind of broken: it does find interest points but right after that it stops with an MethodNotFound exception.

First experiment: failed.

I guess I'm not going to dive into the code to find out what the problem is. Eventually I'll play around a little with ImageJ (there are some dependencies, maybe they weren't loaded properly or so).

* thesis/experiment 1/foto set 1

Stephan Saalfeld - ImageJ Plugins - JavaSIFT.


Rapid Object Detection using a Boosted Cascade of Simple Features

[...] a machine learning approach for visual
object detection which is capable of processing images
extremely rapidly and achieving high detection rates

violaJones_CVPR2001.pdf (application/pdf-Objekt).



SURF (Speeded Up Robust Features) is a robust image detector & descriptor, first presented by Herbert Bay et al. in 2006, that can be used in computer vision tasks like object recognition or 3D reconstruction. It is partly inspired by the SIFT descriptor. The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT. SURF is based on sums of approximated 2D Haar wavelet responses and makes an efficient use of integral images.

via SURF (Wikipedia)